A Weighted Window Approach to Neural Network Time Series Forecasting

Abstract

In this chapter, we propose a neural network based weighted window approach to time series forecasting. We compare the weighted window approach with two commonly used methods of rolling and moving windows in modeling time series. Seven economic data sets are used to compare the performance of these three data windowing methods on observed forecast errors. We find that the proposed approach can improve forecasting performance over traditional approaches.